Sensitivity Analysis for Informatively Interval-Censored Discrete Time-to-Event Data

Michelle Shardell, Johns Hopkins Bloomberg School of Public Health
Daniel O. Scharfstein, Johns Hopkins Bloomberg School of Public Health
Noya Galai, Johns Hopkins Bloomberg School of Public Health
David Vlahov, New York Academy of Science
Samuel A. Bozzette, San Diego Veterans Affairs Medical Center


In many prospective studies, subjects are evaluated for the occurrence of an absorbing event of interest (e.g., HIV infection) at baseline and at a common set of pre-specified visit times after enrollment. Since subjects often miss scheduled visits, the underlying visit of first detection may be interval censored, or more generally, coarsened. Interval-censored data are usually analyzed using the non-identifiable coarsening at random (CAR) assumption. In some settings, the visit compliance and underlying event time processes may be associated, in which case CAR is violated. To examine the sensitivity of inference, we posit a class of models that express deviations from CAR. These models are indexed by non-identifiable, interpretable parameters, which describe the relationship between visit compliance and event times. Plausible ranges for these parameters require eliciting information from scientific experts. For each model, we use the EM algorithm to estimate marginal distributions and proportional hazards model regression parameters. The performance of our method is assessed via a simulation study. We also present analyses of two studies: AIDS Clinical Trial Group (ACTG) 181, a natural history study of cytomegalovirus shedding among advanced AIDS patients, and AIDS Link to the Intravenous Experience (ALIVE), an observational study of HIV infection among intravenous drug users. A sensitivity analysis of study results is performed using information elicited from substantive experts who worked on ACTG 181 and ALIVE.